{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "edefed61-0d80-40c1-95fa-0a8edbbb8331",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hpcq_\\.conda\\envs\\cvxpyenv\\lib\\site-packages\\arctic-1.80.0-py3.8.egg\\arctic\\store\\_pandas_ndarray_store.py:8: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n"
     ]
    }
   ],
   "source": [
    "from fundopt.fundtsloader import getTSLoader\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0bcb76f7-c073-460f-b3c3-3845e37f049b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-01         NaN\n",
       "2020-01-02         NaN\n",
       "2020-01-03         NaN\n",
       "2020-01-06         NaN\n",
       "2020-01-07         NaN\n",
       "                ...   \n",
       "2021-05-10    0.001697\n",
       "2021-05-11    0.001693\n",
       "2021-05-12    0.001691\n",
       "2021-05-13    0.001689\n",
       "2021-05-14    0.001686\n",
       "Freq: B, Name: return, Length: 358, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = dt.date(2020, 1, 1)\n",
    "end   = dt.date(2021, 5, 14)\n",
    "holding = 20\n",
    "# test for MM funds\n",
    "loader = getTSLoader('000198')\n",
    "loader.load(start, end)\n",
    "\n",
    "loader.getReturnTS(start, end, holding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "12c510ac-9137-4450-a632-c904ed636c0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fund_ret = _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "84f3a27f-3d34-498e-b0bb-8282345d222c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fund_ret.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d757abfd-8a7e-4dd7-91ba-cc66b98131a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader2 = getTSLoader('020005')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "185a3ae6-63f0-4a6e-b59f-b2f6009dfd03",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader2.load(start, end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e0e8dbbd-d949-45d3-af4c-a1f4e4e9066b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-05-14 00:00:00', freq='B')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loader2._rawData.index.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8efde522-0d4a-4e1a-99f2-744ee09dca3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ret20 = loader2.getReturnTS(start, end, 20)\n",
    "ret10 = loader2.getReturnTS(start, end, 10)\n",
    "ret5 = loader2.getReturnTS(start, end, 5)\n",
    "ret1 = loader2.getReturnTS(start, end, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "342c659e-8860-465c-8344-0a4488d53be3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret20.plot()\n",
    "ret10.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "704926c7-b522-4b11-a07b-76e2f07764da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "87837cd8-53df-49d2-8f85-3d590031b828",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_pickle('2020-01-01_2021-05-14_20.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e9d1654c-bd81-4850-b1cc-99056210e888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>return</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-29</th>\n",
       "      <td>0.001908</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>0.001906</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>0.001904</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-03</th>\n",
       "      <td>0.001900</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-04</th>\n",
       "      <td>0.001900</td>\n",
       "      <td>-0.282906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05</th>\n",
       "      <td>0.001909</td>\n",
       "      <td>-0.236865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-06</th>\n",
       "      <td>0.001907</td>\n",
       "      <td>-0.262891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <td>0.001906</td>\n",
       "      <td>-0.303005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>0.001901</td>\n",
       "      <td>-0.363804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11</th>\n",
       "      <td>0.001897</td>\n",
       "      <td>-0.272388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              return         0\n",
       "2020-01-01       NaN       NaN\n",
       "2020-01-02       NaN       NaN\n",
       "2020-01-03       NaN       NaN\n",
       "2020-01-06       NaN       NaN\n",
       "2020-01-07       NaN       NaN\n",
       "2020-01-08       NaN       NaN\n",
       "2020-01-09       NaN       NaN\n",
       "2020-01-10       NaN       NaN\n",
       "2020-01-13       NaN       NaN\n",
       "2020-01-14       NaN       NaN\n",
       "2020-01-15       NaN       NaN\n",
       "2020-01-16       NaN       NaN\n",
       "2020-01-17       NaN       NaN\n",
       "2020-01-20       NaN       NaN\n",
       "2020-01-21       NaN       NaN\n",
       "2020-01-22       NaN       NaN\n",
       "2020-01-23       NaN       NaN\n",
       "2020-01-24       NaN       NaN\n",
       "2020-01-27       NaN       NaN\n",
       "2020-01-28       NaN       NaN\n",
       "2020-01-29  0.001908       NaN\n",
       "2020-01-30  0.001906       NaN\n",
       "2020-01-31  0.001904       NaN\n",
       "2020-02-03  0.001900       NaN\n",
       "2020-02-04  0.001900 -0.282906\n",
       "2020-02-05  0.001909 -0.236865\n",
       "2020-02-06  0.001907 -0.262891\n",
       "2020-02-07  0.001906 -0.303005\n",
       "2020-02-10  0.001901 -0.363804\n",
       "2020-02-11  0.001897 -0.272388"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([fund_ret, ret2], axis=1).head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0228b435-0bc2-44d6-9579-2fe21ea9af11",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader2._rawData.to_csv('020005.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6fcf3a71-4304-4607-87ca-7709929ac4dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='NAV', ylabel='ACC_NAV'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vjsVobt2lLTRFSsCR/3E/uzrTl723porVV2nHu2LKNRpqJ+92Jnf1VXjyc/3dvWL28K6vHUhHPL7PuY54nPragRFFJCJdplz9cMZEAbDk308IL5g+KtdCgkPc/YDkawhwKHA1iU7vG8IIMCxDB9ewcPZEBlTHGFLTjwHVMRbOnqhWhUjEVqx7jdd2Zl4g8ITDhmrHuxDkuzbUgcDFJBYR/L/Ah929pXhhRWPm5JGcMG4Yza27qK8dqEQhUgIuuP2pjGWDqo3Fc6eEGE3flesx1DDga8CngVuBo939zTACi8rQwTVKEiIl4uyb/pixrB+w9jszwgumj8vVstgI7AD+D/A2cIHZu9Ms3P364oUmIn3Z4pUb+cvGzH+bPnjJSSFGI7mSxX/ybge3HgqKSCha2tpZcO+zGcvH171H/RQhyzUp78p8vsTMLnP3awKJSET6vNNv+H3W8ke07Hjo8t4pL4dPBfQ9ItLH/fLPr7A9y+inH549McRopEtQ8yS0bJeIFOzS36zmrsbmjOUHvacfZx4zKmO5FE9QLYss24+IiOS26PcvZU0Uhx5Qw9OXfyzEiCRVUMlCLQsR6bWWtnaufeD5jOWjamv48zdPCzEi6S6ox1C/Duh7RKQP+vGK9cQzlH135gQ++89jQ41H9pe1ZWFmC1NWf009f4mZXdd17O7fK0ZwIlL5Fq/cyM+f2Ji27LQj6pQoSkSux1AfBxalOX8DoLWARaQgueZTXHeW9tAuFbmShbv7fq3D5Dn1U4hIQY67+tGMZf/z5MO09E4JyZUs3jaz8d1PJs/tKk5IItIXfPG2VXRmGEcZAz5/4mGhxiPZ5ergvhx4wMy+C3Qt/dhAYpe8i4sYl4hUsKvvW8dDz23PWD7/9CPUqigxuZb7eMDMzgS+AXw5efpZYLa7P1Pk2ESkAn30+sd5cftbGcs/OGIIc08+PMSIJB+5ligfAGxz9/O7nT/YzAa4e+bNcEVEujnxukfZ1Jp5X/v+MfjtV7WabCnK1WfxI+DENOenAT8IPhwRqVQr1r2WNVF87MiDefF7GmRZqnIli6nufnf3k+6+GCgo/ZvZQWb2iJmtT/6szVDvVjPbbmaZx9eJSMn77ZotGcu+MHUst5z/4RCjkZ7KlSyyDY8tdKmQ+cAKdx8PrEgep/NzYHqB1xKRiB08ZEDa8yPfW8OCj08IORrpqVy/8Leb2bHdTybP7Sjw2rOA25LvbwPOTFfJ3f8AvF7gtUQkQotXbuTWP72Stuya2VpyvBzkGjr7DeAuM/s5+w6dnQOcU+C1h7v7VgB332pmBxfyZWY2F5gLMHr06AJDE5GgLF65MeMs7eoq46hD3xtyRNIbWVsW7v4X4DgSj6P+B9A1Kup8EgkjKzN71MyeTfOaVWDc6WJd5O4N7t5QV1cX9NeLSC80vtLC5UvTJ4r+Vcb3PzVJ8ynKRM5VZ919G3CFmR0NnEsiUZwELMnjsxnXFDazbWY2ItmqGAFknqEjImXni7c38tC6bWnL+lUZy79yovbRLiO55lm8n8TjpnOBFuBOwNw9iA1wl5FIPNcmfy4N4DtFpAR88sY/8vSmNzOWXzXzKCWKMpOrg/t54FTgDHef6u4/BvYGdO1rgWlmtp7EvI1rAczsUDNb3lXJzH4FPAH8k5k1m9kFAV1fRIqg8ZWWjImiyuDqMz/AZ457X8hRSaFyPYaaTaJl8ZiZPQjcQUCrzbp7C4lE1P38FmBGyvG5QVxPRMJxyZ1NGcvunDuFhrFDwwtGApOrg/sed/80cATwOHAJMNzMbjKzj4YQn4iUkcZXWtj0RvpVgKZPGK5EUcbymljn7m+5+2J3/zhQDzSReRKdiPRRc372ZNrzhwzpz81zGkKORoLU41nY7v66u9/i7qcUIyARKU9fXvwUb2fYoOK/zzsm5GgkaIUu2SEiwld+9TS/fea1tGW1A/vp8VMFULIQkYJcfd86lq3emrH8ijO07lMlyDkpT0Qkk0t/3cRdT23OWD58SDVnHjMqxIikWNSyEJFe2bBtZ9ZEccYHD+HJBRo0WSnUshCRXrns7sw7K8+aNIIbzlWndiVRshCRHrvl9y+xamNr2rIvTB2r/SkqkB5DiUiPtLS1s/ChF9KWffh9tUoUFUrJQkR65OG1r7E3nn4+xTWf/GDI0UhY9BhKRPJ2+b3PcPvKv6UtO+2Ig7WSbAVTy0JE8tL4SkvGRFFlcN1Z2h61kilZiEhOS5s2c85P06/7VBWDH3x6sna8q3B6DCUiWbW0tTNvyRo696bvp7jzC1p2vC9Qy0JEsmpu3UV1LP2vijnHj1ai6CPUshCRrOprB9IRj+9zrl+Vccfnj1Oi6EPUshCRrIYOrmHh7IkMqI4xpKYfA6pjXP+pSUoUfYxaFiKSVktbO82tu6ivHcjMySM5Ydywd47Vmd33RJYszOwg4E5gDPAqcLa7t3arMwq4HTgEiAOL3P2GcCMV6XuWNm1m3pI1VMdidMTjLJw9kZmTRypJ9GFRPoaaD6xw9/HACtJv09oJfM3djwSmAF8yM60lIFJEXaOfdnfE2dneye6OOJcuWUNLW3vUoUmEokwWs4Dbku9vA87sXsHdt7r708n3O4HngJFhBSjSF6Ub/VQdi9HcuiuiiKQURJkshrv7VkgkBeDgbJXNbAxwNJB2ZpCZzTWzRjNr3LFjR9CxivQZ6UY/dcTj1NcOjCgiKQVFTRZm9qiZPZvmNauH3zMYWAJc7O7/SFfH3Re5e4O7N9TV1QURvkif0tLWzupNbwDsN/pp4eyJ6q/o44rawe3up2UqM7NtZjbC3bea2Qhge4Z61SQSxWJ3v7tIoYr0aek6tP807xSNfpJ3RPkYahlwfvL9+cDS7hXMzICfAc+5+/UhxibSZ2zYtpNv/Hr1fh3aAJNGHahEIUC0yeJaYJqZrQemJY8xs0PNbHmyzgnA54BTzKwp+ZoRTbgilWdp02Zm/PiP7Om27pM6tKW7yOZZuHsLcGqa81uAGcn3fwQs5NBE+oSuIbJ7OuP7lalDW7rTch8ifVSmBQL7V5k6tGU/Wu5DpI9KN0S2f78Yy788VTveyX7UshDpo9ItEPhfZ01UopC01LIQ6cO0QKDkS8lCpI8bOrhGSUJy0mMokT5iw7ad/KZxExu27Yw6FClDalmI9AGX3/sMt6/82zvHc44fzbdnfTDCiKTcqGUhUuE2bNu5T6IAuP2Jv6mFIT2iZCFS4ZqSiwPme14kHSULkQo3edSBPTovko6ShUiFGzd8CHOOH73PuTnHj9Z8CukRdXCL9AHfnvVB5kwZQ9OmN5g86kAlCukxJQuRPmLc8CFKEtJregwlUmG6drxraWuPOhSpIGpZiFSQdDvezZw8MuqwpAKoZSFSIbr2p+i+451aGBIEJQuRCrF2y5vEbN+9wrTjnQRFj6FEKsDSps1c+pvVtHfuuz2qdryToKhlIVLmuh4/dU8UNf1i2vFOAqOWhUiZ69oedTfv7nr3nuoqbv7chzjp/XURRiaVJLKWhZkdZGaPmNn65M/aNHUGmNlfzGy1ma01s6uiiFWklKXbHjWOc9ShB0QUkVSiKB9DzQdWuPt4YEXyuLt24BR3nwRMBqab2ZTwQhQpfem2R9XjJwlalI+hZgH/knx/G/A4MC+1grs70JY8rE6+9n0wKyLaHlWKLspkMdzdtwK4+1YzOzhdJTOrAp4CxgE3uvuTGerNBeYCjB49Ol0VkYqm7VGlmIqaLMzsUeCQNEUL8v0Od98LTDazA4F7zOwD7v5smnqLgEUADQ0Nan2IiASoqMnC3U/LVGZm28xsRLJVMQLYnuO73jCzx4HpwH7JQqQvaGlr16MmiUSUj6GWAecD1yZ/Lu1ewczqgI5kohgInAZcF2qUIiVC6z5JlKIcDXUtMM3M1gPTkseY2aFmtjxZZwTwmJmtAVYBj7j7fZFEKxIhrfskUYusZeHuLcCpac5vAWYk368Bjg45NJGSk27iXde6T3ocJWHQch8iZSDdxDut+yRhUrIQKWFdGxkBmngnkdLaUCIlKl2H9p/mnaLRUBIJtSxESlCmDm2ASaMOVKKQ0ClZiJSgrg7tVNrISKKkZCFSgtShLaVGyUKkBGklWSk16uAWKVFaSVZKiZKFSAnTSrJSKvQYSkREclKyEBGRnJQsREQkJyULERHJSclCRERyUrIQEZGclCxERCQnJQuRkHUtO65d7qScaFKeSIi0j7aUK7UsREKifbSlnEWWLMzsIDN7xMzWJ3/WZqlbZWZ/NbP7woxRJEhadlzKWZQti/nACncfD6xIHmfyVeC5UKISKRItOy7lLMpkMQu4Lfn+NuDMdJXMrB74V+Cn4YQlUhxadlzKWZQd3MPdfSuAu281s4Mz1PshcCkwJKzARIpFy45LuSpqsjCzR4FD0hQtyPPzHwe2u/tTZvYvOerOBeYCjB49umeBioRIy45LOSpqsnD30zKVmdk2MxuRbFWMALanqXYCMNPMZgADgAPM7Jfu/tk011oELAJoaGjwYP4LREQEou2zWAacn3x/PrC0ewV3v8zd6919DHAO8Lt0iUJERIorymRxLTDNzNYD05LHmNmhZrY8wrhERKSbyDq43b0FODXN+S3AjDTnHwceL3pgIiKyH83gFhGRnMy98vqCzWwHsDHqOJKGAX+POogeKKd4yylWULzFVk7xlmqs73P3unQFFZksSomZNbp7Q9Rx5Kuc4i2nWEHxFls5xVtOsXbRYygREclJyUJERHJSsii+RVEH0EPlFG85xQqKt9jKKd5yihVQn4WIiORBLQsREclJyUJERHJSsghAPrv+mdkoM3vMzJ4zs7Vm9tWUsivNbLOZNSVf+81gL6FY897hMKx4k/VuNbPtZvZst/Oh3duA4i3V+zvdzF4wsw1mNj/lfNHvb6Zrp5Sbmf0oWb7GzI7J97PFUGC8r5rZM8l72RhGvHlzd70KfAELgfnJ9/OB69LUGQEck3w/BHgRmJA8vhL4epnEmvPzYcebLDsJOAZ4ttv50O5tQPGW3P0FqoCXgMOA/sDqsP7tZrt2Sp0ZwAOAAVOAJ/P9bCnFmyx7FRgW1r/XnrzUsghGzl3/3H2ruz+dfL+TxDaxI8MKMEWhsea1w2GA8rqeu/8BeL3IseSj0HhL8f4eC2xw95fdfQ9wR/JzYcjn2rOA2z1hJXBgctuDKOIuJN6SpmQRjH12/QMy7foHgJmNAY4Gnkw5fVGySXprkR89FBprjz4fgCCuF9a9hcLjLcX7OxLYlHLczL5/6BTz/ua6drY6+Xw2aIXEC+DAw2b2lCU2dCsZUW6rWlaswF3/Ur5nMLAEuNjd/5E8fRPwHRL/UL4DfB/4txKNNXBBxZtBoPcWih5v4AKI19Kc6xpzH/j97cG1c9XJ57NBKyRegBPcfYsltpl+xMyeT7ZCI6dkkScvfNc/zKyaxC/fxe5+d8p3b0up8xPgvlKNFcjr82HHm+W7A723ye8sWryU5v1tBkalHNcDW5LfHfj9zffaedTpn8dng1ZIvHhiiwbcfbuZ3UPisVZJJAs9hgpGzl3/zMyAnwHPufv13cpSn1d+AthnhEzACoo1n88HrKDrhXxvofD7U4r3dxUw3szGmll/ErtWLoNQ7m/Ga6dYBsxJjjKaAryZfKSWz2eD1ut4zWyQmQ0BMLNBwEcp/r/X/EXdw14JL2AosAJYn/x5UPL8ocDy5PupJJqaa4Cm5GtGsuwXwDPJsmXAiBKONe3no4w3efwrYCvQQeIvtwvCvrcBxVuq93cGiVFxLwELUs4X/f6muzZwIXBh8r0BNybLnwEacsVd5Hvaq3hJjKBanXytDSvefF9a7kNERHLSYygREclJyUJERHJSshARkZyULEREJCclCxERyUnJQiRAZuZm9v2U46+b2ZXd6qw2s18l3w8ysxYze2+3Ovea2dmhBC2SByULkWC1A580s2HpCs3sSBL/351kZoPc/S3gYVIW8EsmjqkEPxtapNeULESC1Ulif+VLMpSfR2Ii28PAzOS5X5GY6dvlE8CD7v52sYIU6SklC5Hg3Qh8pvujpaRPA3eSSBDnJs89CHzIzIYmj89JlouUDCULkYB5YoXe24GvpJ43sw8DO9x9I4mlNY4xs1pP7HuwDDgr+fhqMomWh0jJULIQKY4fAhcAg1LOnQscYWavklgX6ABgdrKs61HUWcBSd+8ILVKRPChZiBSBu78O3EUiYWBmMeBTwER3H+PuY0jsmNb1KOoxYDzwJfQISkqQkoVI8Xwf6BoVdRKw2d03p5T/AZiQ3E8iTmL/kKGUyP4FIqm06qyIiOSkloWIiOSkZCEiIjkpWYiISE5KFiIikpOShYiI5KRkISIiOSlZiIhITv8fhe21PC4Sg3YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loader2._rawData.diff().plot.scatter('NAV', 'ACC_NAV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4a0d7289-831d-4e70-8092-a690fd72924b",
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_nav = loader2._rawData.diff().dropna() # remove the first entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ef571670-0709-4e3d-88ca-a028d17a7c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = diff_nav['NAV']\n",
    "y = diff_nav['ACC_NAV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "64b117e4-536f-4cd8-af79-b77aac8e842c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8d5545c9-3c72-43d5-9e8d-e5c2a68fc458",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = linregress(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3865f279-3216-4e43-9696-299bdd3baab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "b, a = res.intercept, res.slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a351de75-c0d5-434f-a4a8-1e6f5ea97658",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = a*x+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9b58d2fe-78fa-4668-8452-9069f4636d94",
   "metadata": {},
   "outputs": [],
   "source": [
    "residual = y - y_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e2e9518e-334b-4f50-95c7-967faccdd66c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ab2111f2-42d0-4166-b0e1-c97f34e55380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd334a57dc0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y_hat)\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f9aa6271-ada3-4edf-a3c6-c1d4fcfa6709",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import HuberRegressor, TheilSenRegressor, RANSACRegressor, LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0f0d1b63-9d40-41cc-a389-f3c95dce2a77",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear = LinearRegression(fit_intercept=False)\n",
    "huber = HuberRegressor(fit_intercept=False)\n",
    "theil = TheilSenRegressor(fit_intercept=False)\n",
    "ransac = RANSACRegressor(base_estimator=LinearRegression(fit_intercept=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "66843f0d-070c-4485-9e5a-e6bed2f5e5f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = x[:, None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d43990b1-623c-4c8e-81f6-3a985357644a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.70112827]), 0.0)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear.fit(X, y)\n",
    "linear.coef_, linear.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6e6bba18-e1f3-47df-be65-0cff5bc6fab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4.]), 0.0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huber.fit(X, y)\n",
    "huber.coef_, huber.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "78479a84-899e-4381-b21c-859ba2779060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4.]), 0.0)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theil.fit(X, y)\n",
    "theil.coef_, theil.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19b3c926-5d79-4b3c-ae1a-f72c03813eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "theil.score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4197f3c9-5119-47d3-8d0d-a36bbb60a9f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3.99023127]), 0.0)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ransac.fit(X, y)\n",
    "ransac.estimator_.coef_, ransac.estimator_.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "32a3745c-8cc9-45d1-84b3-f4b82bd36079",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "y = 6\n",
    "s = '{0}+{1}={2}, {1}+{0}={2}'.format(x, y, x+y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a1907615-20f9-40cd-a203-7ea12b3e7626",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'f' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-105-328dd34a981c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'{x:0%db}'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'f' is not defined"
     ]
    }
   ],
   "source": [
    "s = f('{x:0%db}' % 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59829d7f-2012-464f-9bd1-aefe4884f351",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31f957a9-818e-4e8c-ae28-9f6a82cdddda",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3500fb3f-a77f-4ec1-b030-deaba7a0ea19",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d2da907a-3e35-4686-b5ae-749f30faf4ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.000000'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'%f' % "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e07fcdb-7e99-417a-a3d9-834f7b51c1e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4632fe86-602b-47e4-ae4e-cf8c34232b52",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6124802c-86c2-4f2e-a6d7-78f75bad7401",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "671f9656-89a0-4dd9-a1ad-ada3b738a67f",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader3 = getTSLoader('159909')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "08fb1d19-f908-4a5a-964d-40e573f0e1b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader3.load(start, end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0d727611-0307-4908-9680-285f46c8b5ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='NAV', ylabel='ACC_NAV'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loader3._rawData.diff().plot.scatter('NAV', 'ACC_NAV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "56a2e208-2076-4b71-9b4f-3892fe7048af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAV</th>\n",
       "      <th>ACC_NAV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-03-10</th>\n",
       "      <td>-6.1544</td>\n",
       "      <td>0.0079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               NAV  ACC_NAV\n",
       "2021-03-10 -6.1544   0.0079"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loader3._rawData.diff().query('NAV < -1')"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "3bf2b71f-6cb9-4963-b0df-a11be6ff7789",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader3._rawData"
   ]
  }
 ],
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